Application of Data Reconciliation and Fault Detection and Isolation of Ion Chambers in Advanced Heavy Water Reactor

Yellapu, Vidya Sagar; Tiwari, A.P.; Degweker, S.B.

doi:10.1016/j.anucene.2015.05.038

Cited by 8 publications

(16 citation statements)

References 21 publications

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“…Since the operation B T y k = r k 0 resembles the relation Ax = 0 on an average sense, B T is taken equivalent to the constraint model A that shows the relationships among the variables in the measurement vector y [3].…”

Section: B Principal Component Analysismentioning

confidence: 99%

“…2 The MSPCA-based FDI scheme is applied to the ion chamber signals of AHWR, a 920 MW (thermal), vertical, pressure tube type, heavy-water moderated, boiling light-water cooled, natural circulation type reactor [22]. AHWR has 9 excore ion chambers located in the vault water surrounding the reflector [3], [31]. The ion chambers sense the leakage flux entering into the vault water, which in turn is proportional to the core-average neutron flux.…”

Section: E On-line Mspca Algorithmmentioning

confidence: 99%

“…The hardware redundancy among sensors, which is always maintained for continuous safe-operation of the reactor even when some sensors malfunction, plays a vital role in Fault Detection and Isolation (FDI). Analytical redundancy or mathematical relationships among sensor signals can be found through either first principles approach or using statistical techniques like Principal Component Analysis (PCA) [2], [3]. PCA-based FDI, though proved to be highly efficient for sensor fault detection as presented in [3], acts only on the static components of the detector signals to find the possible degradation.…”

Section: Introductionmentioning

confidence: 99%

“…Analytical redundancy or mathematical relationships among sensor signals can be found through either first principles approach or using statistical techniques like Principal Component Analysis (PCA) [2], [3]. PCA-based FDI, though proved to be highly efficient for sensor fault detection as presented in [3], acts only on the static components of the detector signals to find the possible degradation. However, extending PCA-based FDI to the process condition monitoring and diagnostics in addition to the sensor anomalies will be of more interest.…”

Section: Introductionmentioning

confidence: 99%

“…This limits their application, as the information hidden in cross-correlation is left unused, which plays a vital role in the detection of sensor faults. With the objective of detecting sensor faults, PCA which captures cross-correlations among the variables has been widely used in nuclear engineering [3], [11]- [13]. In most of the reported works, PCA models are developed for the underlying process assuming its existence only at a single scale in measurement space.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Online Fault Detection and Isolation in Advanced Heavy Water Reactor Using Multiscale Principal Component Analysis

Yellapu¹,

Vajpayee

Tiwari

2019

IEEE Trans. Nucl. Sci.

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Signals from sensors placed at different locations are used for control and protection purposes in a nuclear reactor. It also requires in-service Fault Detection and Isolation (FDI) for its safe operation. The sensor signals are generally a superimposition of low-frequency components representative of true values of the variables being monitored; occasional high-frequency periodical oscillations due to disturbances and faults; and sensor faults. Techniques like Principal Component Analysis (PCA) can be used for FDI, however, it would be more meaningful if the FDI technique can also help for predictive maintenance of reactor internals through vibration spectra. To address these issues, a multi-scale PCA, integrating wavelet transform with PCA and aiming to reduce the modeling cost by using only a fewer scales that contribute to the monitoring has been proposed in this paper for online FDI of Advanced Heavy Water Reactor (AHWR). A new mathematical formulation of the Generalized Likelihood Ratio Test for its use with wavelet approximation coefficients has also been proposed for better sensor-FDI outcomes. The proposed approach detects and isolates sensor faults and process faults using the signals from neutron detectors. Efficacy of the proposed technique is established on the simulated ex-core ion chamber data of AHWR considering different scenarios that involve localized frequency contents representative of process faults, slowly developing (incipient) sensor faults, and the simultaneous presence of two or more of these scenarios. Simulation results validate the effectiveness of the proposed scheme for online FDI in the reactor.

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Section: B Principal Component Analysismentioning

confidence: 99%

Section: E On-line Mspca Algorithmmentioning

confidence: 99%